The present invention, in some embodiments thereof, relates to a method of finding perfusion parameters from medical images and, more particularly, but not exclusively, to a method of finding cerebral perfusion parameters from MRI or CT images of the brain.
Various medical imaging modalities can be useful for measuring rates of perfusion of body tissue as a function of position in the tissue, to detect the precise location and extent of deficiencies in perfusion. Such methods are important for evaluating brain tumors, which are often associated with deficiencies in perfusion, and with leakage of blood or components of blood out of the capillaries. They are also important for detecting and localizing strokes by imaging brain tissue, since effective treatment of strokes can depend critically on rapid diagnosis. These imaging modalities can be used to measure perfusion parameters by introducing an appropriate contrast agent into the bloodstream of a subject, and creating images showing the subsequent concentration of the contrast agent as a function of time and position in the brain. Image processing methods, performed with a computer, can then use the images to find the values of various hemodynamic parameters as a function of position, for example cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), time to peak contrast agent concentration (TTP), and the peak time of the residue function (TMAX). A localized deficit in CBF, for example, can indicate the presence and location of an ischemic stroke. Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is a commonly used technique for measuring hemodynamic parameters in the brain, using a paramagnetic contrast agent, such as a gadolinium-based contrast agent, for example Gd-DPTA. Such contrast agents primarily affect the effective transverse relaxation time T2* when they are in the bloodstream, and DSC-MRI uses pulse sequences that are weighted to show differences in T2*, or equivalently, changes in the effective transverse relaxation rate R2*, defined as 1/T2*. The most straightforward image processing techniques for obtaining hemodynamic parameters from DSC-MRI images assume that no contrast agent leaks out of the bloodstream into the extravascular extracellular space (EES) of the tissue. That assumption can be checked by seeing whether a significant concentration of contrast agent remains in the tissue about 60 to 90 seconds after the contrast agent is introduced into the bloodstream, since by that time, the contrast agent in the blood has spread out enough to have only a very low concentration.
Other imaging and image processing techniques, also using contrast agent, can be used to detect and measure leakage of blood vessels into the EES, particularly in the brain where such leakage can indicate a breakdown in the blood-brain barrier. For example, paramagnetic contrast agents such as gadolinium-based agents tend to primarily affect T1 when they are located in the EES, and for this reason dynamic contrast enhanced (DCE) MRI, weighted toward differences in T1, is often used to detect leakage from blood vessels in the brain, for example due to brain lesions.
In some medical conditions, such as certain brain tumors, both leakage, and abnormalities in perfusion and other hemodynamic parameters, may be present at the same location, and various models have been proposed, and used to motivate image processing techniques for accurately determining hemodynamic parameters even in the presence of leakage. Some of these models are described in a review paper by Anna K. Heye, Ross D. Culling, Maria del C. Valdés Hernández, Michael J. Thrippleton, and Joanna M. Wardlaw, “Assessment of blood-brain barrier disruption using dynamic contrast-enhanced mri. a systematic review,” NeuroImage: Clinical, 6:262-274, 2014. ISSN2213-1582. doi: http://dx.doi.org/10.1016/j.nicl.2014.09. 002.
R. M. Weisskoff, J. L. Boxerman, A. G. Sorensen, S. F. Kulke, T. A. Campbell, and B. R. Rosen, “Simultaneous blood volume and permeability mapping using a single Gd-based contrast injection,” Proceedings of the Society of Magnetic Resonance Imaging 1994 (Suppl. 1), p. 279, describes a robust strategy for correcting T1 enhancement in Gd-based regional cerebral blood volume (rCBV) maps, in regions where T1 effects are significant, such as tissue with blood-brain barrier breakdown, to produce both corrected rCBV maps as well as permeability maps in clinical studies.
Atle Bjornerud, A Gregory Sorensen, Kim Mouridsen, and Kyrre E Emblem, “T1- and T2*-dominant extravasation correction in DSC-MRI: Part I—theoretical considerations and implications for assessment of tumor hemodynamic properties,” Journal of Cerebral Blood Flow & Metabolism, 31(10):2041-2053, 2011, describes a novel contrast agent extravasation-correction method based on analysis of the tissue residue function for assessment of multiple hemodynamic parameters. The method enables semiquantitative determination of the transfer constant, while being insensitive to variations in tissue mean transit time (MTT).
C. C. Quarles, B. D. Ward, and K. M. Schmainda, “Improving the reliability of obtaining tumor hemodynamic parameters in the presence of contrast agent extravasation.” Magnetic Resonance in Medicine, 53 (6):1307-1316, 2005, describes a new approach to improve the reliability of dynamic susceptibility contrast MRI for the evaluation of brain tumor hemodynamics in the presence of contrast agent extravasation. The model-based technique simultaneously estimates the voxel-wise tumor residue function and the temporal extravasation T1 changes following contrast agent leakage. With these estimates the model corrects the measured MRI signal, which is then used to calculate tumor hemodynamic parameters. Quarles et al point out some disadvantages of using the method of Weisskoff et al, cited above, and state that, in Weiskoff et al, “it is assumed that a leaky tumor ΔR2*(t) can be modeled as a scaled version of ΔR2*(t) from healthy tissue. Given that tumor hemodynamics are extremely heterogeneous across and within the same tumor, this is probably not a good assumption. Thus we hypothesize that an improved estimate must allow for this heterogeneity.”
Additional background art includes Ona Wu, Leif Østergaard, Robert M. Weisskoff, Thomas Benner, Bruce R. Rosen, and A. Gregory Sorensen, “Tracer arrival timing insensitive technique for estimating flow in mr perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix,” Magnetic Resonance in Medicine, 50(1):164-174, 2003; Andreas Fieselmann, Markus Kowarschik, Arundhuti Ganguly, Joachim Hornegger, and Rebecca Fahrig, “Deconvolution-based ct and mr brain perfusion measurement: Theoretical model revisited and practical implementation details,” International Journal of Biomedical Imaging, 2011: 20, 2011; Paul Meier and Kenneth L. Zierler, “On the theory of the indicator dilution method for measurement of blood flow and volume,” Journal of Applied Physiology, 6(12):731-744, 1954; A. A. Chan and S. J. Nelson, “Simplified gamma-variate fitting of perfusion curves,” Biomedical Imaging: Nano to Macro, 2004, IEEE International Symposium on, pages 1067-1070 Vol. 2, April 2004; Henrik B. W. Larsson, Frédéric Courivaud, Egill Rostrup, and Adam E. Hansen, “Measurement of brain perfusion, blood volume, and blood brain barrier permeability, using dynamic contrast-enhanced t1 weighted mri at 3 tesla,” Magnetic Resonance in Medicine, 62(5): 1270-1281, 2009; J. L. Boxerman, K. M. Schmainda, and R. M. Weisskoff, “Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not,”. American Journal of Neuroradiology, 27(4): 859-867, 2006; and published U. S. patent application 2011/0257519 to Bjornerud et al.